Echocardiogram video segmentation plays an important role in cardiac disease
diagnosis. This paper studies the unsupervised domain adaption (UDA) for
echocardiogram video segmentation, where the goal is to generalize the model
trained on the source domain to other unlabelled target domains. Existing UDA
segmentation methods are not suitable for this task because they do not model
local information and the cyclical consistency of heartbeat. In this paper, we
introduce a newly collected CardiacUDA dataset and a novel GraphEcho method for
cardiac structure segmentation. Our GraphEcho comprises two innovative modules,
the Spatial-wise Cross-domain Graph Matching (SCGM) and the Temporal Cycle
Consistency (TCC) module, which utilize prior knowledge of echocardiogram
videos, i.e., consistent cardiac structure across patients and centers and the
heartbeat cyclical consistency, respectively. These two modules can better
align global and local features from source and target domains, improving UDA
segmentation results. Experimental results showed that our GraphEcho
outperforms existing state-of-the-art UDA segmentation methods. Our collected
dataset and code will be publicly released upon acceptance. This work will lay
a new and solid cornerstone for cardiac structure segmentation from
echocardiogram videos. Code and dataset are available at:
this https URL

该研究针对超声心动图视频分割，通过无监督领域自适应方法建立了 CardiacUDA 数据集，并引入了一种名为 GraphEcho 的新方法，利用心动周期一致性以及超声心动图视频的先验知识，通过空间交叉域图匹配和时间周期一致性模块，实现了对心脏结构的更好分割，实验证明 GraphEcho 优于现有的 UDA 分割方法。

GraphEcho: 基于图的无监督心房超声分割领域适应

GraphEcho: Graph-Driven Unsupervised Domain Adaptation for  Echocardiogram Video Segmentation

Learning to model how the world changes as time elapses has proven a
challenging problem for the computer vision community. We propose a
self-supervised solution to this problem using temporal cycle consistency
jointly in vision and language, training on narrated video. Our model learns
modality-agnostic functions to predict forward and backward in time, which must
undo each other when composed. This constraint leads to the discovery of
high-level transitions between moments in time, since such transitions are
easily inverted and shared across modalities. We justify the design of our
model with an ablation study on different configurations of the cycle
consistency problem. We then show qualitatively and quantitatively that our
approach yields a meaningful, high-level model of the future and past. We apply
the learned dynamics model without further training to various tasks, such as
predicting future action and temporally ordering sets of images. Project page:
this https URL

提出了一种基于自监督训练的计算机视觉方法，通过结合视觉和语言的时间循环一致性来学习模型预测未来和过去的动态变化，并可应用于不同任务，如动作预测和图像排序。